Internet advertising generates revenue at a large number of online sites. Brokers intermediate between advertisers and online sites to facilitate the display the advertisements to a targeted audience. One example of such a broker is DoubleClick, Inc.
Existing web advertisement selection engines use a number of partially successful techniques to target customers. Typically, the effectiveness of these techniques is measured in terms of audience response rates. Audience response, also referred to as “click through”, is evaluated by counting the number or frequency of users that click on a “banner advertisement” contained on a web page presented to the user.
Clicking on a banner advertisement at a web site typically takes the user to the web site of the advertiser, where additional information about the advertised product or service is provided.
Chickering et al, in D M Chickering and D. Heckerman, “Targeted advertising with inventory management”, pages 145-149, Proceedings of the 2nd ACM Conference on Electronic Commerce, Oct. 17 to 20, 2000, Minneapolis, United States, describe a technique to determine a targeted advertising schedule. The targeted advertising schedule is based on known attributes of each visitor, and attempts to improve audience response given advertisement inventory management constraints. The schedule comprises the number of times an advertisement is to be shown to a particular group of users during a specified time period.
Existing web advertising techniques that target advertisements to users emphasise the user or user profile characteristics. These existing techniques ignore the characteristics of the web marketing space (WMS), as well as the characteristics of the advertisements.
Further, existing techniques do not take into account a series of WMS or a series of advertisements simultaneously. Also, they do not maximise across different marketing types, but only for advertisements. Neither do they optimise across different users or user segments. Consequently, they do not make the optimal use of the available WMS.
Showing a combination or series of marketing messages to a user can sometimes better achieve an advertiser's objective. For example, to promote a wristwatch, a series of three advertisements may be shown: (i) an advertisement of the watch itself; (ii) another advertisement of the watch involving a celebrity; and finally (iii) a coupon for purchasing the watch. Such series of promotional messages can enhance the chances of the user accepting the coupon, and entice the user to buy the watch.
Systems have also been proposed to determine the most suitable product to recommend to a given user. Lawrence et al, in R D Lawrence, G S Almasi, V Kotlyar, M S Viveros, S S Duri, “Personalization of Supermarket Product Recommendations”, RC 21792, IBM Research Report, characterise customers and products on the same attribute space and then compute product recommendations for individual customers.
Existing techniques used by recommender systems involve: content-based filtering, collaborative filtering and association rules. Content-based filtering systems recommend items based on their similarity to what a particular person has liked in the past. Typically, both items and profiles are represented as vectors in a space of features and a similarity is computed between them. Collaborative filtering recommends items that other people, similar to the person in question to whom the items are recommended, have liked. Collaborative filtering uses clustering of customer ratings and explicit or implicit preferences.
Techniques based on association rules compute frequent item sets from the past transaction data. Association rules involve rules of the type: a implies b, to recommend b to the customer who buys a.
Existing product advertising techniques involve user and/or product/content characteristics to target the recommendation to the user, without taking into account the manner in which the advertisement is shown.
Related to existing advertising and product recommendation systems, are proposed systems for electronic coupons. Anand et al, in Rangachari Anand, Manoj Kumar and Anant Jhingran, in “Distributing E-Coupons on the Internet”, Proceedings of INET, June 1999, describe an e-coupon delivery system that offers e-coupons to shoppers at the appropriate stage of the shopper's visit to an online storefront on the Internet.
Anand et al discuss how shopper activity in an electronic storefront can be monitored to infer resistance to or propensity for buying various merchandise. This information can be used to decide: which e-coupons to offer, the terms of the e-coupon, and when the e-coupons should be offered. This technique takes the user characteristics, web page attributes and different marketing objectives when selecting which e-coupon to show on a web page into account.
U.S. Pat. No. 6,134,532, issued Oct. 17, 2000 to Lazarus et al, describes a system and method for selecting and presenting to users personally targeted entities such as advertising, coupons, products and information content. Selection and presentation is based on tracking observed behavior on a user-by-user basis, and using an adaptive vector space representation for both information and behavior information.
The system described in Lazarus et al matches users to entities in a manner that improves with increased operation and observation of user behavior. User behavior, entities (that is, advertisements, coupons, products etc) and information are all represented as content vectors in a unified vector space. Though the system attempts to optimise across different marketing message types, the system only takes into account the attributes of the marketing message and the user, rather than the environment in which the messages are delivered. For a given environment, the system selects a preferred message to show to the user. The system updates the user and attributes as the session progresses.
In view of the above, a need clearly exists for improved marketing techniques that at least attempt to address one or more limitations of prior art approaches.